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# coding=utf-8
# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" MASC Dataset"""
# This script has been adopted from this dataset: "mozilla-foundation/common_voice_11_0"
import csv
import os
import json
import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm
_CITATION = """\
@INPROCEEDINGS{10022652,
author={Al-Fetyani, Mohammad and Al-Barham, Muhammad and Abandah, Gheith and Alsharkawi, Adham and Dawas, Maha},
booktitle={2022 IEEE Spoken Language Technology Workshop (SLT)},
title={MASC: Massive Arabic Speech Corpus},
year={2023},
volume={},
number={},
pages={1006-1013},
doi={10.1109/SLT54892.2023.10022652}}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
MASC is a dataset that contains 1,000 hours of speech sampled at 16 kHz and crawled from over 700 YouTube channels. The dataset is multi-regional, multi-genre, and multi-dialect intended to advance the research and development of Arabic speech technology with a special emphasis on Arabic speech recognition.
"""
_HOMEPAGE = "https://ieee-dataport.org/open-access/masc-massive-arabic-speech-corpus"
_LICENSE = "https://creativecommons.org/licenses/by/4.0/"
_BASE_URL = "https://huggingface.co/datasets/pain/MASC/resolve/main/"
_AUDIO_URL1 = _BASE_URL + "audio/{split}/{split}_{shard_idx}.tar.gz"
_AUDIO_URL2 = _BASE_URL + "audio/{split}/{split}_{shard_idx}.tar.xz"
_TRANSCRIPT_URL = _BASE_URL + "transcript/{split}/{split}.csv"
class MASC(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"video_id": datasets.Value("string"),
"start": datasets.Value("float64"),
"end": datasets.Value("float64"),
"duration": datasets.Value("float64"),
"text": datasets.Value("string"),
"type": datasets.Value("string"),
"file_path": datasets.Value("string"),
"audio": datasets.features.Audio(sampling_rate=16_000),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
version=self.config.version,
)
def _split_generators(self, dl_manager):
n_shards = {"train": 8,"dev": 1, "test": 1}
audio_urls = {}
splits = ("train", "dev", "test")
for split in splits:
audio_urls[split] = [
_AUDIO_URL2.format(split=split, shard_idx="{:02d}".format(i+1)) if split=="train" else _AUDIO_URL1.format(split=split, shard_idx="{:02d}".format(i+1)) for i in range(n_shards[split])
]
archive_paths = dl_manager.download(audio_urls)
local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
meta_urls = {split: _TRANSCRIPT_URL.format(split=split) for split in splits}
meta_paths = dl_manager.download(meta_urls)
split_generators = []
split_names = {
"train": datasets.Split.TRAIN,
"dev": datasets.Split.VALIDATION,
"test": datasets.Split.TEST,
}
for split in splits:
split_generators.append(
datasets.SplitGenerator(
name=split_names.get(split, split),
gen_kwargs={
"local_extracted_archive_paths": local_extracted_archive_paths.get(split),
"archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
"meta_path": meta_paths[split],
},
),
)
return split_generators
def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
data_fields = list(self._info().features.keys())
metadata = {}
with open(meta_path, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_NONE)
for row in reader:
if not row["file_path"].endswith(".wav"):
row["file_path"] += ".wav"
for field in data_fields:
if field not in row:
row[field] = ""
metadata[row["file_path"]] = row
for i, audio_archive in enumerate(archives):
for filename, file in audio_archive:
_, filename = os.path.split(filename)
if filename in metadata:
result = dict(metadata[filename])
# set the audio feature and the path to the extracted file
path = os.path.join(local_extracted_archive_paths[i], filename) if local_extracted_archive_paths else filename
try:
result["audio"] = {"path": path, "bytes": file.read()}
except ReadError as e:
# Handle the ReadError
print("An error occurred while reading the data:", str(e))
continiue
# set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
result["file_path"] = path if local_extracted_archive_paths else filename
yield path, result |